Such upper bound can be estimated on the level of extracted features. Generally speaking, class separability of the feature space directly corresponds to accuracy of the estimate. In the context of soft biometrics [1]}, distinctiveness has to do with collision, or equivalently interference, which describes the event where any two or more subjects belong in the same category of soft biometrics (e.g., female, dark hair, tall) [2]}, [3]}, [4]}, [5]}. We note that this is related to the Birthday paradox [6]} and named works answered questions such as: How large can a population become before it is likelier than not that at least two persons in the group collide biometrically?
